Combining Associative Computing and Distributed Arithmetic methods for efficient implementation of multiple inner products

Many multimedia processing algorithms as well as communication algorithms implemented in mobile devices are based on intensive implementation of linear algebra methods, in particular, implying implementation of a large number of inner products in real time. Among most efficient approaches to perform inner products are the Associative Computing (ASC) approach and Distributed Arithmetic (DA) approach. In ASC, computations are performed on Associative Processors (ASP), where Content-Addressable memories (CAMs) are used instead of traditional processing elements to perform basic arithmetic operations. In the DA approach, computations are reduced to look-up table reads with respect to binary planes of inputs. In this work, we propose a modification of Associative processors that supports efficient implementation of the DA method. Thus, the two powerful methods are combined to further improve the efficiency of multiple inner product computation. Computational complexity analysis of the proposed method illustrates significant speed-up when computing multiple inner products as compared both to the pure ASC method and to the pure DA method as well as to other state-of the art traditional methods for inner product calculation.